Overview

Dataset statistics

Number of variables23
Number of observations768
Missing cells963
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory252.8 KiB
Average record size in memory337.0 B

Variable types

Categorical7
Numeric14
DateTime1
Boolean1

Dataset

DescriptionQuality-verified clinical data for JHB_DPHRU_013
CreatorHEAT Research Programme
AuthorRP2 Clinical Data Team
URLhttps://github.com/Logic06183/RP2_dataoverview

Variable descriptions

study_sourceStudy identifier
Age (at enrolment)Patient age at study enrollment
SexBiological sex
RaceRacial/ethnic group
enrollment_dateDate of study enrollment
visit_dateDate of clinic visit
primary_datePrimary reference date
study_armStudy treatment arm
study_visitStudy visit number
Antiretroviral Therapy StatusCurrent ART status
BMI (kg/m²)Body Mass Index
weight_kgBody weight in kilograms
height_mHeight in meters
Waist circumference (cm)Waist circumference in centimeters
hip_circumference_cmHip circumference in centimeters
waist_hip_ratioWaist-to-hip ratio
systolic_bp_mmHgSystolic blood pressure
diastolic_bp_mmHgDiastolic blood pressure
heart_rate_bpmHeart rate in beats per minute
Respiratory rate (breaths/min)Respiratory rate
Oxygen saturation (%)Oxygen saturation
body_temperature_celsiusBody temperature in Celsius
CD4 cell count (cells/µL)CD4+ T lymphocyte count
HIV viral load (copies/mL)HIV RNA copies per mL
cd4_percentCD4+ percentage
cd8_count_cells_uLCD8+ T lymphocyte count
cd4_cd8_ratioCD4/CD8 ratio
Hematocrit (%)Hematocrit
hemoglobin_g_dLHemoglobin concentration
White blood cell count (×10³/µL)Total WBC count
Red blood cell count (×10⁶/µL)Total RBC count
Platelet count (×10³/µL)Platelet count
MCV (MEAN CELL VOLUME)Mean corpuscular volume
mch_pgMean corpuscular hemoglobin
mchc_g_dLMean corpuscular hemoglobin concentration
RDWRed cell distribution width
Lymphocyte count (×10⁹/L)Lymphocyte absolute count
Neutrophil count (×10⁹/L)Neutrophil absolute count
Monocyte count (×10⁹/L)Monocyte absolute count
Eosinophil count (×10⁹/L)Eosinophil absolute count
Basophil count (×10⁹/L)Basophil absolute count
lymphocyte_percentLymphocyte percentage
neutrophil_percentNeutrophil percentage
monocyte_percentMonocyte percentage
eosinophil_percentEosinophil percentage
basophil_percentBasophil percentage
ALT (U/L)Alanine aminotransferase
AST (U/L)Aspartate aminotransferase
Alkaline phosphatase (U/L)Alkaline phosphatase
Total bilirubin (mg/dL)Total bilirubin
direct_bilirubin_mg_dLDirect bilirubin
indirect_bilirubin_mg_dLIndirect bilirubin
Albumin (g/dL)Serum albumin
Total protein (g/dL)Total serum protein
ggt_u_LGamma-glutamyl transferase
creatinine_umol_LSerum creatinine (µmol/L)
creatinine_mg_dLSerum creatinine (mg/dL)
creatinine clearanceEstimated creatinine clearance
bun_mg_dLBlood urea nitrogen
urea_mmol_LSerum urea
egfr_ml_minEstimated glomerular filtration rate
Sodium (mEq/L)Serum sodium
Potassium (mEq/L)Serum potassium
chloride_mEq_LSerum chloride
bicarbonate_mEq_LSerum bicarbonate
calcium_mg_dLSerum calcium
magnesium_mg_dLSerum magnesium
phosphate_mg_dLSerum phosphate
total_cholesterol_mg_dLTotal cholesterol
hdl_cholesterol_mg_dLHDL cholesterol
ldl_cholesterol_mg_dLLDL cholesterol
Triglycerides (mg/dL)Triglycerides
vldl_cholesterol_mg_dLVLDL cholesterol
cholesterol_hdl_ratioTotal cholesterol/HDL ratio
fasting_glucose_mmol_LFasting blood glucose (mmol/L)
glucose_mg_dLBlood glucose (mg/dL)
hba1c_percentGlycated hemoglobin
insulin_uIU_mLSerum insulin
lactate_mmol_LBlood lactate
crp_mg_LC-reactive protein
esr_mm_hrErythrocyte sedimentation rate
pt_secondsProthrombin time
inrInternational normalized ratio
aptt_secondsActivated partial thromboplastin time
uric_acid_mg_dLSerum uric acid
ldh_u_LLactate dehydrogenase
ck_u_LCreatine kinase
amylase_u_LSerum amylase
lipase_u_LSerum lipase
climate_daily_mean_tempDaily mean temperature
climate_daily_max_tempDaily maximum temperature
climate_daily_min_tempDaily minimum temperature
climate_temp_anomalyTemperature anomaly from baseline
climate_heat_day_p90Heat day indicator (>90th percentile)
climate_heat_day_p95Heat day indicator (>95th percentile)
climate_heat_stress_indexHeat stress index
climate_humidityRelative humidity
climate_precipitationPrecipitation
climate_seasonSeason
cd4_correction_appliedQuality flag: CD4 corrections applied
final_comprehensive_fix_appliedQuality flag: Comprehensive corrections applied
waist_circ_unit_correction_appliedQuality flag: Waist circumference unit corrected
sa_biomarker_standardsSouth African biomarker reference standards applied

Alerts

study_source has constant value "JHB_DPHRU_013"Constant
cd4_correction_applied has constant value "0.0"Constant
final_comprehensive_fix_applied has constant value "1.0"Constant
sa_biomarker_standards has constant value "1.0"Constant
climate_heat_day_p95 has constant value "0.0"Constant
BMI (kg/m²) is highly overall correlated with Waist circumference (cm) and 1 other fieldsHigh correlation
Waist circumference (cm) is highly overall correlated with BMI (kg/m²) and 3 other fieldsHigh correlation
climate_daily_max_temp is highly overall correlated with climate_daily_mean_temp and 5 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with climate_daily_max_temp and 3 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with climate_daily_mean_temp and 3 other fieldsHigh correlation
climate_heat_day_p90 is highly overall correlated with Waist circumference (cm) and 7 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with climate_daily_max_temp and 3 other fieldsHigh correlation
climate_season is highly overall correlated with climate_daily_max_temp and 5 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
hdl_cholesterol_mg_dL is highly overall correlated with climate_heat_day_p90 and 1 other fieldsHigh correlation
height_m is highly overall correlated with climate_heat_day_p90 and 1 other fieldsHigh correlation
ldl_cholesterol_mg_dL is highly overall correlated with climate_heat_day_p90 and 1 other fieldsHigh correlation
total_cholesterol_mg_dL is highly overall correlated with climate_heat_day_p90 and 2 other fieldsHigh correlation
waist_circ_unit_correction_applied is highly overall correlated with Waist circumference (cm) and 7 other fieldsHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²) and 3 other fieldsHigh correlation
climate_heat_day_p90 is highly imbalanced (98.6%)Imbalance
Age (at enrolment) has 14 (1.8%) missing valuesMissing
weight_kg has 205 (26.7%) missing valuesMissing
height_m has 331 (43.1%) missing valuesMissing
Waist circumference (cm) has 205 (26.7%) missing valuesMissing
total_cholesterol_mg_dL has 59 (7.7%) missing valuesMissing
hdl_cholesterol_mg_dL has 58 (7.6%) missing valuesMissing
ldl_cholesterol_mg_dL has 58 (7.6%) missing valuesMissing
fasting_glucose_mmol_L has 32 (4.2%) missing valuesMissing

Reproduction

Analysis started2025-11-25 05:10:12.857541
Analysis finished2025-11-25 05:10:20.944067
Duration8.09 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Study identifier

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.5 KiB
JHB_DPHRU_013
768 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters9984
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_DPHRU_013
2nd rowJHB_DPHRU_013
3rd rowJHB_DPHRU_013
4th rowJHB_DPHRU_013
5th rowJHB_DPHRU_013

Common Values

ValueCountFrequency (%)
JHB_DPHRU_013768
100.0%

Length

2025-11-25T07:10:20.964005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:20.994321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_dphru_013768
100.0%

Most occurring characters

ValueCountFrequency (%)
H1536
15.4%
_1536
15.4%
J768
7.7%
B768
7.7%
D768
7.7%
P768
7.7%
R768
7.7%
U768
7.7%
0768
7.7%
1768
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6144
61.5%
Decimal Number2304
 
23.1%
Connector Punctuation1536
 
15.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H1536
25.0%
J768
12.5%
B768
12.5%
D768
12.5%
P768
12.5%
R768
12.5%
U768
12.5%
Decimal Number
ValueCountFrequency (%)
0768
33.3%
1768
33.3%
3768
33.3%
Connector Punctuation
ValueCountFrequency (%)
_1536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6144
61.5%
Common3840
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
H1536
25.0%
J768
12.5%
B768
12.5%
D768
12.5%
P768
12.5%
R768
12.5%
U768
12.5%
Common
ValueCountFrequency (%)
_1536
40.0%
0768
20.0%
1768
20.0%
3768
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H1536
15.4%
_1536
15.4%
J768
7.7%
B768
7.7%
D768
7.7%
P768
7.7%
R768
7.7%
U768
7.7%
0768
7.7%
1768
7.7%

Age (at enrolment)
Real number (ℝ)

Missing 

Patient age at study enrollment

Distinct183
Distinct (%)24.3%
Missing14
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean33.533554
Minimum18.1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.031708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile22
Q127.85
median33.95
Q339
95-th percentile46
Maximum51
Range32.9
Interquartile range (IQR)11.15

Descriptive statistics

Standard deviation7.3527855
Coefficient of variation (CV)0.21926651
Kurtosis-0.80768914
Mean33.533554
Median Absolute Deviation (MAD)5.95
Skewness0.055500259
Sum25284.3
Variance54.063454
MonotonicityNot monotonic
2025-11-25T07:10:21.078354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4032
 
4.2%
3431
 
4.0%
3931
 
4.0%
3729
 
3.8%
3027
 
3.5%
3125
 
3.3%
3823
 
3.0%
4122
 
2.9%
2621
 
2.7%
3521
 
2.7%
Other values (173)492
64.1%
ValueCountFrequency (%)
18.11
 
0.1%
18.81
 
0.1%
193
 
0.4%
19.31
 
0.1%
19.42
 
0.3%
19.51
 
0.1%
19.61
 
0.1%
209
1.2%
20.11
 
0.1%
20.61
 
0.1%
ValueCountFrequency (%)
511
 
0.1%
503
 
0.4%
49.11
 
0.1%
495
0.7%
488
1.0%
47.91
 
0.1%
47.21
 
0.1%
4710
1.3%
46.61
 
0.1%
46.41
 
0.1%

primary_date
Date

Primary reference date

Distinct232
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
Minimum2011-02-10 00:00:00
Maximum2013-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:10:21.122783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:21.172667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BMI (kg/m²)
Real number (ℝ)

High correlation 

Body Mass Index

Distinct248
Distinct (%)32.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.852803
Minimum15.1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.221097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile19.13
Q123
median26.7
Q331.5
95-th percentile40.54
Maximum57
Range41.9
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation6.6900116
Coefficient of variation (CV)0.24019168
Kurtosis1.6551874
Mean27.852803
Median Absolute Deviation (MAD)4.1
Skewness1.0682353
Sum21363.1
Variance44.756255
MonotonicityNot monotonic
2025-11-25T07:10:21.263203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.311
 
1.4%
2510
 
1.3%
21.810
 
1.3%
26.79
 
1.2%
27.48
 
1.0%
25.88
 
1.0%
32.38
 
1.0%
21.58
 
1.0%
22.98
 
1.0%
28.97
 
0.9%
Other values (238)680
88.5%
ValueCountFrequency (%)
15.11
0.1%
15.31
0.1%
161
0.1%
16.11
0.1%
16.61
0.1%
16.81
0.1%
16.91
0.1%
17.11
0.1%
17.21
0.1%
17.31
0.1%
ValueCountFrequency (%)
571
 
0.1%
56.11
 
0.1%
54.91
 
0.1%
54.31
 
0.1%
50.71
 
0.1%
50.41
 
0.1%
50.11
 
0.1%
49.83
0.4%
491
 
0.1%
46.42
0.3%

weight_kg
Real number (ℝ)

High correlation  Missing 

Body weight in kilograms

Distinct360
Distinct (%)63.9%
Missing205
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean69.787744
Minimum35.1
Maximum140.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.307089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35.1
5-th percentile47.61
Q157.9
median67.2
Q378.4
95-th percentile102.99
Maximum140.5
Range105.4
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation16.938157
Coefficient of variation (CV)0.24270962
Kurtosis1.3539238
Mean69.787744
Median Absolute Deviation (MAD)10
Skewness0.98611018
Sum39290.5
Variance286.90115
MonotonicityNot monotonic
2025-11-25T07:10:21.355307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.35
 
0.7%
65.45
 
0.7%
59.64
 
0.5%
544
 
0.5%
53.74
 
0.5%
76.64
 
0.5%
65.64
 
0.5%
61.84
 
0.5%
55.14
 
0.5%
69.44
 
0.5%
Other values (350)521
67.8%
(Missing)205
 
26.7%
ValueCountFrequency (%)
35.11
0.1%
35.81
0.1%
36.41
0.1%
39.81
0.1%
41.61
0.1%
41.81
0.1%
421
0.1%
42.12
0.3%
42.51
0.1%
43.61
0.1%
ValueCountFrequency (%)
140.51
0.1%
135.21
0.1%
133.81
0.1%
130.61
0.1%
129.11
0.1%
121.91
0.1%
1181
0.1%
116.31
0.1%
115.81
0.1%
114.71
0.1%

height_m
Real number (ℝ)

High correlation  Missing 

Height in meters

Distinct194
Distinct (%)44.4%
Missing331
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean1.5870046
Minimum1.39
Maximum1.785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.401359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.39
5-th percentile1.4918
Q11.552
median1.589
Q31.619
95-th percentile1.6772
Maximum1.785
Range0.395
Interquartile range (IQR)0.067

Descriptive statistics

Standard deviation0.057610126
Coefficient of variation (CV)0.036301172
Kurtosis1.2076171
Mean1.5870046
Median Absolute Deviation (MAD)0.034
Skewness-0.018390588
Sum693.521
Variance0.0033189266
MonotonicityNot monotonic
2025-11-25T07:10:21.451658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5847
 
0.9%
1.5917
 
0.9%
1.5887
 
0.9%
1.617
 
0.9%
1.6066
 
0.8%
1.5686
 
0.8%
1.66
 
0.8%
1.5956
 
0.8%
1.5986
 
0.8%
1.5855
 
0.7%
Other values (184)374
48.7%
(Missing)331
43.1%
ValueCountFrequency (%)
1.391
0.1%
1.4041
0.1%
1.4051
0.1%
1.4061
0.1%
1.4161
0.1%
1.4171
0.1%
1.4571
0.1%
1.461
0.1%
1.4661
0.1%
1.4671
0.1%
ValueCountFrequency (%)
1.7851
0.1%
1.781
0.1%
1.7621
0.1%
1.7591
0.1%
1.7571
0.1%
1.7331
0.1%
1.7171
0.1%
1.7151
0.1%
1.711
0.1%
1.7081
0.1%

Waist circumference (cm)
Real number (ℝ)

High correlation  Missing 

Waist circumference in centimeters

Distinct115
Distinct (%)20.4%
Missing205
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean89.362345
Minimum2.9
Maximum915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.499851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile67.55
Q178
median86.5
Q396.5
95-th percentile115.25
Maximum915
Range912.1
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation38.256862
Coefficient of variation (CV)0.42810942
Kurtosis387.32681
Mean89.362345
Median Absolute Deviation (MAD)8.5
Skewness17.935657
Sum50311
Variance1463.5875
MonotonicityNot monotonic
2025-11-25T07:10:21.544160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8722
 
2.9%
8519
 
2.5%
8119
 
2.5%
7818
 
2.3%
8918
 
2.3%
8617
 
2.2%
7916
 
2.1%
7416
 
2.1%
7615
 
2.0%
7713
 
1.7%
Other values (105)390
50.8%
(Missing)205
26.7%
ValueCountFrequency (%)
2.91
 
0.1%
8.11
 
0.1%
10.81
 
0.1%
591
 
0.1%
612
0.3%
621
 
0.1%
634
0.5%
641
 
0.1%
652
0.3%
664
0.5%
ValueCountFrequency (%)
9151
0.1%
1511
0.1%
1451
0.1%
143.51
0.1%
1401
0.1%
1331
0.1%
1311
0.1%
1301
0.1%
129.51
0.1%
1282
0.3%

total_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

Total cholesterol

Distinct331
Distinct (%)46.7%
Missing59
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean4.1249083
Minimum1.12
Maximum10.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.587609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile2.538
Q13.39
median4
Q34.81
95-th percentile6.016
Maximum10.48
Range9.36
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.1613229
Coefficient of variation (CV)0.28153907
Kurtosis3.3551237
Mean4.1249083
Median Absolute Deviation (MAD)0.7
Skewness1.0133115
Sum2924.56
Variance1.3486708
MonotonicityNot monotonic
2025-11-25T07:10:21.632605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410
 
1.3%
4.119
 
1.2%
3.627
 
0.9%
4.576
 
0.8%
3.896
 
0.8%
3.486
 
0.8%
4.936
 
0.8%
3.686
 
0.8%
2.775
 
0.7%
3.525
 
0.7%
Other values (321)643
83.7%
(Missing)59
 
7.7%
ValueCountFrequency (%)
1.121
0.1%
1.221
0.1%
1.291
0.1%
1.381
0.1%
1.541
0.1%
1.591
0.1%
1.82
0.3%
1.851
0.1%
2.012
0.3%
2.061
0.1%
ValueCountFrequency (%)
10.481
0.1%
10.291
0.1%
9.282
0.3%
9.041
0.1%
8.651
0.1%
7.71
0.1%
7.591
0.1%
7.31
0.1%
7.281
0.1%
6.821
0.1%

hdl_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

HDL cholesterol

Distinct174
Distinct (%)24.5%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.1211127
Minimum0.28
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.675745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.51
Q10.83
median1.07
Q31.37
95-th percentile1.8855
Maximum3.7
Range3.42
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.44352229
Coefficient of variation (CV)0.39560902
Kurtosis4.4712255
Mean1.1211127
Median Absolute Deviation (MAD)0.26
Skewness1.2913394
Sum795.99
Variance0.19671202
MonotonicityNot monotonic
2025-11-25T07:10:21.721193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.813
 
1.7%
1.0413
 
1.7%
0.8513
 
1.7%
0.9313
 
1.7%
1.113
 
1.7%
1.1811
 
1.4%
0.9511
 
1.4%
110
 
1.3%
0.8410
 
1.3%
0.879
 
1.2%
Other values (164)594
77.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
0.281
0.1%
0.321
0.1%
0.332
0.3%
0.342
0.3%
0.351
0.1%
0.362
0.3%
0.372
0.3%
0.391
0.1%
0.42
0.3%
0.412
0.3%
ValueCountFrequency (%)
3.73
0.4%
2.81
 
0.1%
2.532
0.3%
2.491
 
0.1%
2.441
 
0.1%
2.311
 
0.1%
2.31
 
0.1%
2.291
 
0.1%
2.242
0.3%
2.231
 
0.1%

ldl_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

LDL cholesterol

Distinct261
Distinct (%)36.8%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.6717042
Minimum0
Maximum6.04
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.764665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6745
Q11.11
median1.535
Q32.07
95-th percentile3.18
Maximum6.04
Range6.04
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.77008108
Coefficient of variation (CV)0.4606563
Kurtosis1.8978142
Mean1.6717042
Median Absolute Deviation (MAD)0.475
Skewness1.0866871
Sum1186.91
Variance0.59302488
MonotonicityNot monotonic
2025-11-25T07:10:21.810277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.019
 
1.2%
1.129
 
1.2%
1.329
 
1.2%
1.378
 
1.0%
1.298
 
1.0%
1.187
 
0.9%
2.067
 
0.9%
1.947
 
0.9%
1.267
 
0.9%
1.767
 
0.9%
Other values (251)632
82.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
01
 
0.1%
0.331
 
0.1%
0.391
 
0.1%
0.422
 
0.3%
0.451
 
0.1%
0.461
 
0.1%
0.471
 
0.1%
0.53
0.4%
0.555
0.7%
0.564
0.5%
ValueCountFrequency (%)
6.041
0.1%
4.411
0.1%
4.281
0.1%
4.252
0.3%
4.191
0.1%
4.131
0.1%
3.971
0.1%
3.941
0.1%
3.891
0.1%
3.871
0.1%

fasting_glucose_mmol_L
Real number (ℝ)

Missing 

Fasting blood glucose (mmol/L)

Distinct276
Distinct (%)37.5%
Missing32
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.928356
Minimum0.95
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:21.854980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile3.35
Q14.5
median4.93
Q35.4125
95-th percentile6.12
Maximum15
Range14.05
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.95305831
Coefficient of variation (CV)0.1933826
Kurtosis19.566982
Mean4.928356
Median Absolute Deviation (MAD)0.45
Skewness1.5235001
Sum3627.27
Variance0.90832015
MonotonicityNot monotonic
2025-11-25T07:10:21.899485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.219
 
1.2%
4.759
 
1.2%
5.428
 
1.0%
4.828
 
1.0%
4.938
 
1.0%
4.78
 
1.0%
5.248
 
1.0%
4.737
 
0.9%
4.577
 
0.9%
5.177
 
0.9%
Other values (266)657
85.5%
(Missing)32
 
4.2%
ValueCountFrequency (%)
0.951
0.1%
1.121
0.1%
1.371
0.1%
1.471
0.1%
2.021
0.1%
2.041
0.1%
2.211
0.1%
2.221
0.1%
2.261
0.1%
2.552
0.3%
ValueCountFrequency (%)
151
0.1%
9.911
0.1%
9.671
0.1%
8.241
0.1%
7.971
0.1%
7.621
0.1%
7.611
0.1%
7.421
0.1%
7.271
0.1%
7.061
0.1%

cd4_correction_applied
Categorical

Constant 

Quality flag: CD4 corrections applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T07:10:21.942443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:21.975411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

final_comprehensive_fix_applied
Categorical

Constant 

Quality flag: Comprehensive corrections applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
1.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0768
100.0%

Length

2025-11-25T07:10:22.088327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:22.119935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1768
50.0%
0768
50.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

waist_circ_unit_correction_applied
Boolean

High correlation 

Quality flag: Waist circumference unit corrected

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
True
563 
False
205 
ValueCountFrequency (%)
True563
73.3%
False205
 
26.7%
2025-11-25T07:10:22.149327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

sa_biomarker_standards
Categorical

Constant 

South African biomarker reference standards applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
1.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0768
100.0%

Length

2025-11-25T07:10:22.185243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:22.217844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1768
50.0%
0768
50.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Daily mean temperature

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.209534
Minimum8.507
Maximum21.131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:22.248120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.507
5-th percentile13.316
Q114.603
median16.425
Q317.799
95-th percentile20.357
Maximum21.131
Range12.624
Interquartile range (IQR)3.196

Descriptive statistics

Standard deviation2.3493444
Coefficient of variation (CV)0.14493596
Kurtosis0.37172559
Mean16.209534
Median Absolute Deviation (MAD)1.74
Skewness-0.23360933
Sum12448.922
Variance5.519419
MonotonicityNot monotonic
2025-11-25T07:10:22.286514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
16.42578
10.2%
17.03971
 
9.2%
17.79970
 
9.1%
15.6757
 
7.4%
14.20956
 
7.3%
14.60354
 
7.0%
13.31653
 
6.9%
14.68549
 
6.4%
20.35749
 
6.4%
17.54146
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
8.5072
 
0.3%
9.61621
 
2.7%
13.31653
6.9%
13.6563
 
0.4%
14.20956
7.3%
14.5534
4.4%
14.60354
7.0%
14.68549
6.4%
14.86240
5.2%
15.6757
7.4%
ValueCountFrequency (%)
21.1311
 
0.1%
20.4656
 
0.8%
20.35749
6.4%
20.2931
 
0.1%
19.59943
5.6%
19.08434
4.4%
17.79970
9.1%
17.54146
6.0%
17.03971
9.2%
16.42578
10.2%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Daily maximum temperature

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.380574
Minimum14.624
Maximum28.861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:22.320880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14.624
5-th percentile18.896
Q120.108
median20.768
Q325.325
95-th percentile25.931
Maximum28.861
Range14.237
Interquartile range (IQR)5.217

Descriptive statistics

Standard deviation2.9122833
Coefficient of variation (CV)0.13012549
Kurtosis-1.5446717
Mean22.380574
Median Absolute Deviation (MAD)2.695
Skewness0.0051205477
Sum17188.281
Variance8.4813938
MonotonicityNot monotonic
2025-11-25T07:10:22.357904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
25.93178
10.2%
20.32471
 
9.2%
25.870
 
9.1%
19.12557
 
7.4%
19.27756
 
7.3%
20.58954
 
7.0%
20.76853
 
6.9%
18.89649
 
6.4%
24.31949
 
6.4%
25.00546
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
14.6242
 
0.3%
17.34421
 
2.7%
18.89649
6.4%
19.12557
7.4%
19.27756
7.3%
20.10834
4.4%
20.32471
9.2%
20.58954
7.0%
20.76853
6.9%
21.4743
 
0.4%
ValueCountFrequency (%)
28.8611
 
0.1%
26.7691
 
0.1%
26.13634
4.4%
25.93178
10.2%
25.870
9.1%
25.5726
 
0.8%
25.32543
5.6%
25.00546
6.0%
24.31949
6.4%
23.46340
5.2%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Daily minimum temperature

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.377281
Minimum3.333
Maximum17.507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:22.392583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.333
5-th percentile6.306
Q17.463
median9.869
Q312.877
95-th percentile17.507
Maximum17.507
Range14.174
Interquartile range (IQR)5.414

Descriptive statistics

Standard deviation3.3901858
Coefficient of variation (CV)0.32669307
Kurtosis-0.53633939
Mean10.377281
Median Absolute Deviation (MAD)3.008
Skewness0.26308779
Sum7969.752
Variance11.49336
MonotonicityNot monotonic
2025-11-25T07:10:22.430511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6.30678
10.2%
14.29171
 
9.2%
10.49370
 
9.1%
12.87757
 
7.4%
8.7656
 
7.3%
9.00454
 
7.0%
6.61653
 
6.9%
11.18749
 
6.4%
17.50749
 
6.4%
9.86946
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
3.33321
 
2.7%
3.9932
 
0.3%
6.0343
 
0.4%
6.30678
10.2%
6.61653
6.9%
7.46340
5.2%
8.03534
4.4%
8.7656
7.3%
9.00454
7.0%
9.86946
6.0%
ValueCountFrequency (%)
17.50749
6.4%
16.5761
 
0.1%
14.29171
9.2%
14.05743
5.6%
13.9681
 
0.1%
13.4546
 
0.8%
12.87757
7.4%
12.46534
4.4%
11.18749
6.4%
10.49370
9.1%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Temperature anomaly from baseline

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.931276
Minimum-0.093
Maximum11.984
Zeros0
Zeros (%)0.0%
Negative57
Negative (%)7.4%
Memory size12.0 KiB
2025-11-25T07:10:22.468951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.093
5-th percentile-0.093
Q13.719
median6.276
Q37.913
95-th percentile11.984
Maximum11.984
Range12.077
Interquartile range (IQR)4.194

Descriptive statistics

Standard deviation3.4869388
Coefficient of variation (CV)0.58789016
Kurtosis-0.79783053
Mean5.931276
Median Absolute Deviation (MAD)1.637
Skewness-0.0032101025
Sum4555.22
Variance12.158742
MonotonicityNot monotonic
2025-11-25T07:10:22.508023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
11.98478
10.2%
1.18571
 
9.2%
10.02570
 
9.1%
-0.09357
 
7.4%
6.27656
 
7.3%
4.85554
 
7.0%
7.91353
 
6.9%
2.16449
 
6.4%
3.71949
 
6.4%
7.43446
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
-0.09357
7.4%
1.18571
9.2%
2.16449
6.4%
3.71949
6.4%
4.85554
7.0%
4.934
4.4%
5.18934
4.4%
5.3976
 
0.8%
6.27656
7.3%
6.3643
5.6%
ValueCountFrequency (%)
11.98478
10.2%
10.02570
9.1%
9.8393
 
0.4%
9.3861
 
0.1%
7.91353
6.9%
7.7621
 
2.7%
7.61940
5.2%
7.43446
6.0%
7.3112
 
0.3%
6.5051
 
0.1%

climate_heat_day_p90
Categorical

High correlation  Imbalance 

Heat day indicator (>90th percentile)

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
767 
0.571
 
1

Length

Max length5
Median length3
Mean length3.0026042
Min length3

Characters and Unicode

Total characters2306
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0767
99.9%
0.5711
 
0.1%

Length

2025-11-25T07:10:22.549618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:22.585332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0767
99.9%
0.5711
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1538
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01535
99.8%
51
 
0.1%
71
 
0.1%
11
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

climate_heat_day_p95
Categorical

Constant 

Heat day indicator (>95th percentile)

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T07:10:22.620062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:22.652073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Heat stress index

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.523178
Minimum2.218
Maximum24.693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T07:10:22.682700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.218
5-th percentile14.373
Q115.36
median16.691
Q320.175
95-th percentile21.262
Maximum24.693
Range22.475
Interquartile range (IQR)4.815

Descriptive statistics

Standard deviation2.7052804
Coefficient of variation (CV)0.15438298
Kurtosis1.0512418
Mean17.523178
Median Absolute Deviation (MAD)1.757
Skewness-0.1762757
Sum13457.801
Variance7.3185422
MonotonicityNot monotonic
2025-11-25T07:10:22.718578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
21.05778
10.2%
16.47671
 
9.2%
19.95870
 
9.1%
15.08857
 
7.4%
14.93456
 
7.3%
16.76554
 
7.0%
15.72153
 
6.9%
14.37349
 
6.4%
20.51849
 
6.4%
21.26246
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
2.2182
 
0.3%
13.35121
 
2.7%
13.4283
 
0.4%
14.37349
6.4%
14.93456
7.3%
15.08857
7.4%
15.3634
4.4%
15.72153
6.9%
16.44234
4.4%
16.47671
9.2%
ValueCountFrequency (%)
24.6931
 
0.1%
22.8676
 
0.8%
22.5261
 
0.1%
21.26246
6.0%
21.05778
10.2%
20.51849
6.4%
20.17543
5.6%
19.95870
9.1%
16.76554
7.0%
16.69140
5.2%

climate_season
Categorical

High correlation 

Season

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Autumn
445 
Spring
120 
Winter
104 
Summer
99 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowAutumn
3rd rowSummer
4th rowAutumn
5th rowAutumn

Common Values

ValueCountFrequency (%)
Autumn445
57.9%
Spring120
 
15.6%
Winter104
 
13.5%
Summer99
 
12.9%

Length

2025-11-25T07:10:22.756175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:22.792999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
autumn445
57.9%
spring120
 
15.6%
winter104
 
13.5%
summer99
 
12.9%

Most occurring characters

ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3840
83.3%
Uppercase Letter768
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u989
25.8%
n669
17.4%
m643
16.7%
t549
14.3%
r323
 
8.4%
i224
 
5.8%
e203
 
5.3%
p120
 
3.1%
g120
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A445
57.9%
S219
28.5%
W104
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin4608
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Interactions

2025-11-25T07:10:20.120587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.035983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.682041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.151171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.595538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.059965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.781405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.299872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.783326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.371566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.950218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.616252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.085368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.612017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.154596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.220896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.713175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.188354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.629905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.093813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.825918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.334187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.815476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.410143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.987299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.650863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.119916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.648903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.183254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.272424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.741469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.216293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.659451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.123357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.859545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.365672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.850328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.440578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.017702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.681954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.151109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.678336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.217523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.313378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.770906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.246357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.692038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.153993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.898770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.398045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.954838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.470739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.050258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.713137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.184746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.711027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.252358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.346869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.804671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.279434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.726813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.187872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.941209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.434813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.025638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.503947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.084503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.752426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.220974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.747373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.286286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.377599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.850771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.310096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.758562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.215011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.986632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.466611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.064281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.558607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.115557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.783221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.251077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.776412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.315781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.407972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.889812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.338985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.789811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.243619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.024452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.500036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.097440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.681295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.206393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.813183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.281750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.807621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.347317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.442183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.924670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.371741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.823590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.276959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.066887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.535970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.131606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.715786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.291902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.846676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.316482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.871557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.381889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.474631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.957842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.403592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.855159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.310128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.102662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.571432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.167622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.749788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.327973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.880558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.348758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.907043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.416904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.510228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.990185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.433774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.888369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.342344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.137988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.609057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.203005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.783280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.361514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.913362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.384476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.941899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.451369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.543525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.022766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.466730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.921995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.390253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.169734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.643264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.235771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.814596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.396005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.948413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.418531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.979126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.505499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.579792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.055924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.497177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.953374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.484688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.200924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.676341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.272205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.847467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.517510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.982804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.453169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.016988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.539775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.615858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.090538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.530030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.987317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.582939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.236526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.712934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.306036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.882123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.550945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.018410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.522894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.054893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.572143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:13.649474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.121133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:14.560975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.023713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:15.629428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.267742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:16.751649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.341085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:17.916905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:18.584977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.051575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:19.576464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:20.088322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T07:10:22.826693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)Waist circumference (cm)climate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_day_p90climate_heat_stress_indexclimate_seasonclimate_temp_anomalyfasting_glucose_mmol_Lhdl_cholesterol_mg_dLheight_mldl_cholesterol_mg_dLtotal_cholesterol_mg_dLwaist_circ_unit_correction_appliedweight_kg
Age (at enrolment)1.0000.2290.2850.0510.017-0.0250.0730.0110.0140.0720.1540.012-0.0370.1570.1550.0000.218
BMI (kg/m²)0.2291.0000.8950.0360.0400.0300.0000.0410.0000.0070.1160.020-0.1120.1060.0960.0000.937
Waist circumference (cm)0.2850.8951.0000.0250.0600.0591.0000.0310.044-0.0330.1740.0040.0470.0910.1111.0000.896
climate_daily_max_temp0.0510.0360.0251.0000.631-0.0840.9960.7620.6320.649-0.049-0.098-0.0870.054-0.0320.6600.047
climate_daily_mean_temp0.0170.0400.0600.6311.0000.6630.0850.6290.730-0.023-0.0080.079-0.0690.1650.0820.4850.046
climate_daily_min_temp-0.0250.0300.059-0.0840.6631.0000.0910.0330.678-0.7030.0700.189-0.0450.1720.1010.6290.037
climate_heat_day_p900.0730.0001.0000.9960.0850.0911.0000.3410.0700.9950.0001.0001.0001.0001.0000.0001.000
climate_heat_stress_index0.0110.0410.0310.7620.6290.0330.3411.0000.5590.438-0.132-0.113-0.1100.095-0.0080.5850.032
climate_season0.0140.0000.0440.6320.7300.6780.0700.5591.0000.7540.1850.1500.0620.0920.1430.9080.101
climate_temp_anomaly0.0720.007-0.0330.649-0.023-0.7030.9950.4380.7541.000-0.050-0.1690.020-0.015-0.0930.734-0.001
fasting_glucose_mmol_L0.1540.1160.174-0.049-0.0080.0700.000-0.1320.185-0.0501.0000.008-0.0550.032-0.0660.2270.133
hdl_cholesterol_mg_dL0.0120.0200.004-0.0980.0790.1891.000-0.1130.150-0.1690.0081.000-0.0210.2690.5070.1020.021
height_m-0.037-0.1120.047-0.087-0.069-0.0451.000-0.1100.0620.020-0.055-0.0211.000-0.081-0.0391.0000.185
ldl_cholesterol_mg_dL0.1570.1060.0910.0540.1650.1721.0000.0950.092-0.0150.0320.269-0.0811.0000.5660.0940.093
total_cholesterol_mg_dL0.1550.0960.111-0.0320.0820.1011.000-0.0080.143-0.093-0.0660.507-0.0390.5661.0000.1250.084
waist_circ_unit_correction_applied0.0000.0001.0000.6600.4850.6290.0000.5850.9080.7340.2270.1021.0000.0940.1251.0001.000
weight_kg0.2180.9370.8960.0470.0460.0371.0000.0320.101-0.0010.1330.0210.1850.0930.0841.0001.000

Missing values

2025-11-25T07:10:20.626113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T07:10:20.823278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T07:10:20.900448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceAge (at enrolment)primary_dateBMI (kg/m²)weight_kgheight_mWaist circumference (cm)total_cholesterol_mg_dLhdl_cholesterol_mg_dLldl_cholesterol_mg_dLfasting_glucose_mmol_Lcd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
217JHB_DPHRU_01319.42011-02-1024.259.81.58483.02.771.231.415.030.01.0True1.019.59925.32514.0576.3600.00.020.175Summer
218JHB_DPHRU_01339.42011-04-0933.683.91.589103.04.930.901.544.550.01.0True1.014.60320.5899.0044.8550.00.016.765Autumn
219JHB_DPHRU_013NaN2012-01-2133.1NaNNaNNaN5.111.332.204.760.01.0False1.020.46525.57213.4545.3970.00.022.867Summer
220JHB_DPHRU_01340.02012-04-0233.584.71.598102.05.351.612.376.720.01.0True1.014.68518.89611.1872.1640.00.014.373Autumn
221JHB_DPHRU_01342.02013-05-1630.176.0NaN89.05.891.713.365.680.01.0True1.013.31620.7686.6167.9130.00.015.721Autumn
222JHB_DPHRU_01339.02011-03-1922.068.01.76277.03.971.163.035.030.01.0True1.017.03920.32414.2911.1850.00.016.476Autumn
223JHB_DPHRU_01340.02011-08-2721.5NaNNaNNaN2.520.522.484.320.01.0False1.016.42525.9316.30611.9840.00.021.057Winter
224JHB_DPHRU_01340.02012-02-0921.265.01.75977.04.170.952.715.480.01.0True1.020.35724.31917.5073.7190.00.020.518Summer
225JHB_DPHRU_01341.02013-05-0921.666.1NaN77.04.471.042.605.260.01.0True1.013.31620.7686.6167.9130.00.015.721Autumn
226JHB_DPHRU_01322.22011-03-1719.351.81.64669.03.130.902.174.250.01.0True1.017.03920.32414.2911.1850.00.016.476Autumn
study_sourceAge (at enrolment)primary_dateBMI (kg/m²)weight_kgheight_mWaist circumference (cm)total_cholesterol_mg_dLhdl_cholesterol_mg_dLldl_cholesterol_mg_dLfasting_glucose_mmol_Lcd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
975JHB_DPHRU_01325.92011-06-1121.550.41.53868.04.961.141.574.760.01.0True1.09.61617.3443.3337.7600.00.013.351Winter
976JHB_DPHRU_013NaN2012-01-2122.6NaNNaNNaN6.241.322.194.970.01.0False1.020.46525.57213.4545.3970.00.022.867Summer
977JHB_DPHRU_01327.02012-05-1223.654.61.52572.06.821.962.68NaN0.01.0True1.014.55020.1088.0354.9000.00.016.442Autumn
978JHB_DPHRU_01333.32011-06-1132.477.81.55497.03.701.101.055.380.01.0True1.09.61617.3443.3337.7600.00.013.351Winter
979JHB_DPHRU_01334.02011-11-1634.4NaNNaNNaN5.321.401.725.500.01.0False1.019.08426.13612.4655.1890.00.015.360Spring
980JHB_DPHRU_01334.02012-05-0237.390.81.562115.54.111.761.325.990.01.0True1.014.55020.1088.0354.9000.00.016.442Autumn
981JHB_DPHRU_01335.02013-05-0837.991.1NaN103.02.350.421.356.110.01.0True1.013.31620.7686.6167.9130.00.015.721Autumn
982JHB_DPHRU_01331.32011-06-0731.884.61.630101.03.520.911.005.210.01.0True1.09.61617.3443.3337.7600.00.013.351Winter
983JHB_DPHRU_01332.02011-11-1031.2NaNNaNNaN2.931.020.594.670.01.0False1.019.08426.13612.4655.1890.00.015.360Spring
984JHB_DPHRU_01332.02012-05-0233.287.21.627104.0NaNNaNNaN5.760.01.0True1.014.55020.1088.0354.9000.00.016.442Autumn